Reinforcement Learning (RL) [1]} is a decision-making process where the agent learns to interact with the environment aiming to maximize returns. Recent works include applications in autonomous driving [2]}, robotics [3]}, [4]}, video games [5]}, [6]}, [7]}, [8]}, dialogue assistance [9]}, among others [10]}. Currently, most of the research utilizes virtual environments such as Arcade Learning Environment (ALE) [11]} and DeepMind Control Suite [12]}, where typically a reward function and the state representation are available. However, in real-world problems, the agent usually has no indication of how efficient are the actions it performs in the environment. In other cases, the rewards are sparse and binary, only perceived when the agent achieves the task accomplishment [13]}. Besides this, most real-world scenarios have complex dynamics with tasks consisting of multiple steps and a high-dimensional state space [4]}. However, few works have focused on problems that can be shared and reproduced among the community and grounded in real-world settings. Some examples include robotics applications, where the limitation is having access to the assets.
